Overview

Dataset statistics

Number of variables22
Number of observations7109
Missing cells54
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 MiB
Average record size in memory731.5 B

Variable types

Categorical10
Numeric12

Alerts

PRT_ID has a high cardinality: 7109 distinct values High cardinality
DATE_SALE has a high cardinality: 2798 distinct values High cardinality
DATE_BUILD has a high cardinality: 5808 distinct values High cardinality
INT_SQFT is highly correlated with N_BEDROOM and 4 other fieldsHigh correlation
N_BEDROOM is highly correlated with INT_SQFT and 2 other fieldsHigh correlation
N_BATHROOM is highly correlated with N_BEDROOM and 1 other fieldsHigh correlation
N_ROOM is highly correlated with INT_SQFT and 5 other fieldsHigh correlation
QS_ROOMS is highly correlated with QS_OVERALLHigh correlation
QS_BATHROOM is highly correlated with QS_OVERALLHigh correlation
QS_BEDROOM is highly correlated with QS_OVERALLHigh correlation
QS_OVERALL is highly correlated with QS_ROOMS and 2 other fieldsHigh correlation
REG_FEE is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
COMMIS is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
SALES_PRICE is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
INT_SQFT is highly correlated with N_BEDROOM and 5 other fieldsHigh correlation
N_BEDROOM is highly correlated with INT_SQFT and 2 other fieldsHigh correlation
N_BATHROOM is highly correlated with INT_SQFT and 2 other fieldsHigh correlation
N_ROOM is highly correlated with INT_SQFT and 5 other fieldsHigh correlation
QS_ROOMS is highly correlated with QS_OVERALLHigh correlation
QS_BATHROOM is highly correlated with QS_OVERALLHigh correlation
QS_BEDROOM is highly correlated with QS_OVERALLHigh correlation
QS_OVERALL is highly correlated with QS_ROOMS and 2 other fieldsHigh correlation
REG_FEE is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
COMMIS is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
SALES_PRICE is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
INT_SQFT is highly correlated with N_BEDROOM and 1 other fieldsHigh correlation
N_BEDROOM is highly correlated with INT_SQFT and 2 other fieldsHigh correlation
N_BATHROOM is highly correlated with N_BEDROOM and 1 other fieldsHigh correlation
N_ROOM is highly correlated with INT_SQFT and 3 other fieldsHigh correlation
REG_FEE is highly correlated with N_ROOM and 1 other fieldsHigh correlation
SALES_PRICE is highly correlated with REG_FEEHigh correlation
AREA is highly correlated with INT_SQFT and 7 other fieldsHigh correlation
INT_SQFT is highly correlated with AREA and 6 other fieldsHigh correlation
N_BEDROOM is highly correlated with AREA and 3 other fieldsHigh correlation
N_BATHROOM is highly correlated with AREA and 3 other fieldsHigh correlation
N_ROOM is highly correlated with AREA and 6 other fieldsHigh correlation
BUILDTYPE is highly correlated with REG_FEE and 1 other fieldsHigh correlation
MZZONE is highly correlated with AREAHigh correlation
QS_ROOMS is highly correlated with QS_OVERALLHigh correlation
QS_BATHROOM is highly correlated with QS_OVERALLHigh correlation
QS_BEDROOM is highly correlated with QS_OVERALLHigh correlation
QS_OVERALL is highly correlated with QS_ROOMS and 2 other fieldsHigh correlation
REG_FEE is highly correlated with AREA and 5 other fieldsHigh correlation
COMMIS is highly correlated with AREA and 4 other fieldsHigh correlation
SALES_PRICE is highly correlated with AREA and 5 other fieldsHigh correlation
PRT_ID is uniformly distributed Uniform
DATE_BUILD is uniformly distributed Uniform
PRT_ID has unique values Unique

Reproduction

Analysis started2022-07-05 04:21:03.306750
Analysis finished2022-07-05 04:21:17.715547
Duration14.41 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

PRT_ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7109
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size437.5 KiB
P03210
 
1
P01175
 
1
P07437
 
1
P06628
 
1
P02767
 
1
Other values (7104)
7104 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7109 ?
Unique (%)100.0%

Sample

1st rowP03210
2nd rowP09411
3rd rowP01812
4th rowP05346
5th rowP06210

Common Values

ValueCountFrequency (%)
P032101
 
< 0.1%
P011751
 
< 0.1%
P074371
 
< 0.1%
P066281
 
< 0.1%
P027671
 
< 0.1%
P005971
 
< 0.1%
P062351
 
< 0.1%
P085451
 
< 0.1%
P075341
 
< 0.1%
P072341
 
< 0.1%
Other values (7099)7099
99.9%

Length

2022-07-05T09:51:17.758343image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
p032101
 
< 0.1%
p093701
 
< 0.1%
p053461
 
< 0.1%
p062101
 
< 0.1%
p002191
 
< 0.1%
p091051
 
< 0.1%
p096791
 
< 0.1%
p033771
 
< 0.1%
p096231
 
< 0.1%
p095401
 
< 0.1%
Other values (7099)7099
99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AREA
Categorical

HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size453.8 KiB
Chrompet
1681 
Karapakkam
1363 
KK Nagar
996 
Velachery
979 
Anna Nagar
783 
Other values (12)
1307 

Length

Max length10
Median length8
Mean length8.342382895
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowKarapakkam
2nd rowAnna Nagar
3rd rowAdyar
4th rowVelachery
5th rowKarapakkam

Common Values

ValueCountFrequency (%)
Chrompet1681
23.6%
Karapakkam1363
19.2%
KK Nagar996
14.0%
Velachery979
13.8%
Anna Nagar783
11.0%
Adyar773
10.9%
T Nagar496
 
7.0%
Chrompt9
 
0.1%
Chrmpet6
 
0.1%
Chormpet6
 
0.1%
Other values (7)17
 
0.2%

Length

2022-07-05T09:51:17.817576image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nagar2280
24.3%
chrompet1681
17.9%
karapakkam1363
14.5%
kk996
10.6%
velachery979
10.4%
anna783
 
8.3%
adyar773
 
8.2%
t496
 
5.3%
chrompt9
 
0.1%
chormpet6
 
0.1%
Other values (8)23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

INT_SQFT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1699
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1382.073006
Minimum500
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:17.890433image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile702
Q1993
median1373
Q31744
95-th percentile2084.6
Maximum2500
Range2000
Interquartile range (IQR)751

Descriptive statistics

Standard deviation457.4109025
Coefficient of variation (CV)0.3309600147
Kurtosis-0.8863792596
Mean1382.073006
Median Absolute Deviation (MAD)376
Skewness0.1312376308
Sum9825157
Variance209224.7337
MonotonicityNot monotonic
2022-07-05T09:51:18.101089image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178118
 
0.3%
153815
 
0.2%
150513
 
0.2%
151413
 
0.2%
163412
 
0.2%
78612
 
0.2%
96112
 
0.2%
165512
 
0.2%
108112
 
0.2%
185311
 
0.2%
Other values (1689)6979
98.2%
ValueCountFrequency (%)
5003
< 0.1%
5012
< 0.1%
5021
 
< 0.1%
5042
< 0.1%
5051
 
< 0.1%
5061
 
< 0.1%
5072
< 0.1%
5084
0.1%
5102
< 0.1%
5111
 
< 0.1%
ValueCountFrequency (%)
25001
 
< 0.1%
24991
 
< 0.1%
24981
 
< 0.1%
24971
 
< 0.1%
24963
< 0.1%
24952
< 0.1%
24941
 
< 0.1%
24931
 
< 0.1%
24921
 
< 0.1%
24911
 
< 0.1%

DATE_SALE
Categorical

HIGH CARDINALITY

Distinct2798
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Memory size465.3 KiB
06-10-2009
 
12
06-01-2009
 
10
12-04-2011
 
10
26-02-2012
 
10
17-11-2010
 
10
Other values (2793)
7057 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique988 ?
Unique (%)13.9%

Sample

1st row04-05-2011
2nd row19-12-2006
3rd row04-02-2012
4th row13-03-2010
5th row05-10-2009

Common Values

ValueCountFrequency (%)
06-10-200912
 
0.2%
06-01-200910
 
0.1%
12-04-201110
 
0.1%
26-02-201210
 
0.1%
17-11-201010
 
0.1%
15-03-201210
 
0.1%
19-07-20119
 
0.1%
01-04-20099
 
0.1%
28-02-20129
 
0.1%
17-06-20119
 
0.1%
Other values (2788)7011
98.6%

Length

2022-07-05T09:51:18.174841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06-10-200912
 
0.2%
12-04-201110
 
0.1%
26-02-201210
 
0.1%
17-11-201010
 
0.1%
15-03-201210
 
0.1%
06-01-200910
 
0.1%
17-06-20119
 
0.1%
30-11-20099
 
0.1%
14-08-20109
 
0.1%
13-03-20109
 
0.1%
Other values (2788)7011
98.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DIST_MAINROAD
Real number (ℝ≥0)

Distinct201
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.60317907
Minimum0
Maximum200
Zeros33
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:18.247598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q150
median99
Q3148
95-th percentile190
Maximum200
Range200
Interquartile range (IQR)98

Descriptive statistics

Standard deviation57.40310959
Coefficient of variation (CV)0.5763180465
Kurtosis-1.165240378
Mean99.60317907
Median Absolute Deviation (MAD)49
Skewness0.01814383556
Sum708079
Variance3295.11699
MonotonicityNot monotonic
2022-07-05T09:51:18.322352image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3956
 
0.8%
5153
 
0.7%
7852
 
0.7%
7749
 
0.7%
7348
 
0.7%
15648
 
0.7%
1448
 
0.7%
4947
 
0.7%
11147
 
0.7%
446
 
0.6%
Other values (191)6615
93.1%
ValueCountFrequency (%)
033
0.5%
128
0.4%
244
0.6%
327
0.4%
446
0.6%
536
0.5%
642
0.6%
727
0.4%
831
0.4%
937
0.5%
ValueCountFrequency (%)
20038
0.5%
19930
0.4%
19830
0.4%
19738
0.5%
19636
0.5%
19534
0.5%
19435
0.5%
19329
0.4%
19236
0.5%
19140
0.6%

N_BEDROOM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.6370287
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:18.392400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8029015594
Coefficient of variation (CV)0.4904627264
Kurtosis0.7346203807
Mean1.6370287
Median Absolute Deviation (MAD)0
Skewness1.161882495
Sum11636
Variance0.644650914
MonotonicityNot monotonic
2022-07-05T09:51:18.440071image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
13795
53.4%
22352
33.1%
3707
 
9.9%
4254
 
3.6%
(Missing)1
 
< 0.1%
ValueCountFrequency (%)
13795
53.4%
22352
33.1%
3707
 
9.9%
4254
 
3.6%
ValueCountFrequency (%)
4254
 
3.6%
3707
 
9.9%
22352
33.1%
13795
53.4%

N_BATHROOM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.213260135
Minimum1
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:18.499868image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum2
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4096387078
Coefficient of variation (CV)0.3376346885
Kurtosis-0.03900589183
Mean1.213260135
Median Absolute Deviation (MAD)0
Skewness1.400358943
Sum8619
Variance0.1678038709
MonotonicityNot monotonic
2022-07-05T09:51:18.547235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
15589
78.6%
21515
 
21.3%
(Missing)5
 
0.1%
ValueCountFrequency (%)
15589
78.6%
21515
 
21.3%
ValueCountFrequency (%)
21515
 
21.3%
15589
78.6%

N_ROOM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.688704459
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:18.596067image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum6
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.019098916
Coefficient of variation (CV)0.2762755671
Kurtosis-0.5307863127
Mean3.688704459
Median Absolute Deviation (MAD)1
Skewness0.1188007656
Sum26223
Variance1.038562601
MonotonicityNot monotonic
2022-07-05T09:51:18.644427image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
42563
36.1%
32125
29.9%
51246
17.5%
2921
 
13.0%
6254
 
3.6%
ValueCountFrequency (%)
2921
 
13.0%
32125
29.9%
42563
36.1%
51246
17.5%
6254
 
3.6%
ValueCountFrequency (%)
6254
 
3.6%
51246
17.5%
42563
36.1%
32125
29.9%
2921
 
13.0%

SALE_COND
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size450.0 KiB
AdjLand
1433 
Partial
1429 
Normal Sale
1423 
AbNormal
1406 
Family
1403 
Other values (4)
 
15

Length

Max length11
Median length7
Mean length7.803910536
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAbNormal
2nd rowAbNormal
3rd rowAbNormal
4th rowFamily
5th rowAbNormal

Common Values

ValueCountFrequency (%)
AdjLand1433
20.2%
Partial1429
20.1%
Normal Sale1423
20.0%
AbNormal1406
19.8%
Family1403
19.7%
Adj Land6
 
0.1%
Ab Normal5
 
0.1%
Partiall3
 
< 0.1%
PartiaLl1
 
< 0.1%

Length

2022-07-05T09:51:18.705224image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:18.764129image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
adjland1433
16.8%
partial1429
16.7%
normal1428
16.7%
sale1423
16.7%
abnormal1406
16.5%
family1403
16.4%
adj6
 
0.1%
land6
 
0.1%
ab5
 
0.1%
partiall4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PARK_FACIL
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size413.2 KiB
Yes
3587 
No
3520 
Noo
 
2

Length

Max length3
Median length3
Mean length2.504853003
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes3587
50.5%
No3520
49.5%
Noo2
 
< 0.1%

Length

2022-07-05T09:51:18.831902image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:18.874328image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
yes3587
50.5%
no3520
49.5%
noo2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

DATE_BUILD
Categorical

HIGH CARDINALITY
UNIFORM

Distinct5808
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size465.3 KiB
02-07-1987
 
6
04-04-1999
 
5
27-08-2000
 
4
13-05-1982
 
4
08-04-1989
 
4
Other values (5803)
7086 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4676 ?
Unique (%)65.8%

Sample

1st row15-05-1967
2nd row22-12-1995
3rd row09-02-1992
4th row18-03-1988
5th row13-10-1979

Common Values

ValueCountFrequency (%)
02-07-19876
 
0.1%
04-04-19995
 
0.1%
27-08-20004
 
0.1%
13-05-19824
 
0.1%
08-04-19894
 
0.1%
17-01-19964
 
0.1%
21-11-19924
 
0.1%
06-12-19854
 
0.1%
18-09-19714
 
0.1%
19-07-19774
 
0.1%
Other values (5798)7066
99.4%

Length

2022-07-05T09:51:18.916186image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02-07-19876
 
0.1%
04-04-19995
 
0.1%
02-12-19824
 
0.1%
03-10-19994
 
0.1%
23-01-19874
 
0.1%
14-03-19854
 
0.1%
19-02-19794
 
0.1%
16-01-20034
 
0.1%
29-01-19824
 
0.1%
02-10-19904
 
0.1%
Other values (5798)7066
99.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

BUILDTYPE
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size444.2 KiB
House
2444 
Commercial
2325 
Others
2310 
Other
 
26
Comercial
 
4

Length

Max length10
Median length6
Mean length6.962441975
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCommercial
2nd rowCommercial
3rd rowCommercial
4th rowOthers
5th rowOthers

Common Values

ValueCountFrequency (%)
House2444
34.4%
Commercial2325
32.7%
Others2310
32.5%
Other26
 
0.4%
Comercial4
 
0.1%

Length

2022-07-05T09:51:18.975988image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:19.020837image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
house2444
34.4%
commercial2325
32.7%
others2310
32.5%
other26
 
0.4%
comercial4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

UTILITY_AVAIL
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size434.8 KiB
AllPub
1886 
NoSeWa
1871 
NoSewr
1829 
ELO
1522 
All Pub
 
1

Length

Max length7
Median length6
Mean length5.615135743
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowELO
4th rowNoSewr
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub1886
26.5%
NoSeWa1871
26.3%
NoSewr 1829
25.7%
ELO1522
21.4%
All Pub1
 
< 0.1%

Length

2022-07-05T09:51:19.073734image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:19.116520image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
allpub1886
26.5%
nosewa1871
26.3%
nosewr1829
25.7%
elo1522
21.4%
all1
 
< 0.1%
pub1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

STREET
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size440.9 KiB
Paved
2560 
Gravel
2520 
No Access
2010 
Pavd
 
12
NoAccess
 
7

Length

Max length9
Median length6
Mean length6.486706991
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPaved
2nd rowGravel
3rd rowGravel
4th rowPaved
5th rowGravel

Common Values

ValueCountFrequency (%)
Paved2560
36.0%
Gravel2520
35.4%
No Access2010
28.3%
Pavd12
 
0.2%
NoAccess7
 
0.1%

Length

2022-07-05T09:51:19.167215image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:19.206088image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
paved2560
28.1%
gravel2520
27.6%
no2010
22.0%
access2010
22.0%
pavd12
 
0.1%
noaccess7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MZZONE
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size408.2 KiB
RL
1858 
RH
1822 
RM
1817 
C
550 
A
537 

Length

Max length2
Median length2
Mean length1.773245182
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowRH
3rd rowRL
4th rowI
5th rowC

Common Values

ValueCountFrequency (%)
RL1858
26.1%
RH1822
25.6%
RM1817
25.6%
C550
 
7.7%
A537
 
7.6%
I525
 
7.4%

Length

2022-07-05T09:51:19.261024image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-05T09:51:19.303905image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
rl1858
26.1%
rh1822
25.6%
rm1817
25.6%
c550
 
7.7%
a537
 
7.6%
i525
 
7.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

QS_ROOMS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.517470812
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:19.357235image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.7
median3.5
Q34.3
95-th percentile4.9
Maximum5
Range3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.8919724311
Coefficient of variation (CV)0.2535834635
Kurtosis-1.197535123
Mean3.517470812
Median Absolute Deviation (MAD)0.8
Skewness-0.01895704371
Sum25005.7
Variance0.7956148178
MonotonicityNot monotonic
2022-07-05T09:51:19.421751image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.5265
 
3.7%
3.8259
 
3.6%
3.6255
 
3.6%
4.6252
 
3.5%
3.9245
 
3.4%
4.9242
 
3.4%
3.4240
 
3.4%
4.7239
 
3.4%
3.3239
 
3.4%
4.8239
 
3.4%
Other values (21)4634
65.2%
ValueCountFrequency (%)
2203
2.9%
2.1236
3.3%
2.2213
3.0%
2.3224
3.2%
2.4208
2.9%
2.5265
3.7%
2.6237
3.3%
2.7200
2.8%
2.8226
3.2%
2.9220
3.1%
ValueCountFrequency (%)
5228
3.2%
4.9242
3.4%
4.8239
3.4%
4.7239
3.4%
4.6252
3.5%
4.5218
3.1%
4.4219
3.1%
4.3225
3.2%
4.2239
3.4%
4.1222
3.1%

QS_BATHROOM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.507244338
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:19.494453image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.7
median3.5
Q34.3
95-th percentile4.9
Maximum5
Range3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.8978337054
Coefficient of variation (CV)0.2559940565
Kurtosis-1.21625135
Mean3.507244338
Median Absolute Deviation (MAD)0.8
Skewness0.0003104318578
Sum24933
Variance0.8061053625
MonotonicityNot monotonic
2022-07-05T09:51:19.678841image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.7256
 
3.6%
4.8255
 
3.6%
3.7251
 
3.5%
4.7247
 
3.5%
4.9245
 
3.4%
3241
 
3.4%
4.2237
 
3.3%
4.6234
 
3.3%
3.4234
 
3.3%
2.2234
 
3.3%
Other values (21)4675
65.8%
ValueCountFrequency (%)
2222
3.1%
2.1224
3.2%
2.2234
3.3%
2.3220
3.1%
2.4230
3.2%
2.5233
3.3%
2.6226
3.2%
2.7256
3.6%
2.8206
2.9%
2.9228
3.2%
ValueCountFrequency (%)
5219
3.1%
4.9245
3.4%
4.8255
3.6%
4.7247
3.5%
4.6234
3.3%
4.5231
3.2%
4.4219
3.1%
4.3224
3.2%
4.2237
3.3%
4.1210
3.0%

QS_BEDROOM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.485300324
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:19.748480image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.1
Q12.7
median3.5
Q34.3
95-th percentile4.9
Maximum5
Range3
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.8872664105
Coefficient of variation (CV)0.2545738755
Kurtosis-1.190165265
Mean3.485300324
Median Absolute Deviation (MAD)0.8
Skewness0.01728160906
Sum24777
Variance0.7872416831
MonotonicityNot monotonic
2022-07-05T09:51:19.819243image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2.6273
 
3.8%
3.2253
 
3.6%
4248
 
3.5%
2.4244
 
3.4%
3.8244
 
3.4%
3.1243
 
3.4%
2.1242
 
3.4%
3241
 
3.4%
3.4239
 
3.4%
4.3237
 
3.3%
Other values (21)4645
65.3%
ValueCountFrequency (%)
2221
3.1%
2.1242
3.4%
2.2237
3.3%
2.3200
2.8%
2.4244
3.4%
2.5226
3.2%
2.6273
3.8%
2.7222
3.1%
2.8210
3.0%
2.9219
3.1%
ValueCountFrequency (%)
5217
3.1%
4.9203
2.9%
4.8211
3.0%
4.7228
3.2%
4.6233
3.3%
4.5227
3.2%
4.4237
3.3%
4.3237
3.3%
4.2212
3.0%
4.1223
3.1%

QS_OVERALL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct479
Distinct (%)6.8%
Missing48
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.503253788
Minimum2
Maximum4.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:19.893996image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.63
Q13.13
median3.5
Q33.89
95-th percentile4.37
Maximum4.97
Range2.97
Interquartile range (IQR)0.76

Descriptive statistics

Standard deviation0.5272229035
Coefficient of variation (CV)0.1504952068
Kurtosis-0.4896687645
Mean3.503253788
Median Absolute Deviation (MAD)0.38
Skewness-0.007263226359
Sum24736.475
Variance0.27796399
MonotonicityNot monotonic
2022-07-05T09:51:19.964761image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5459
 
0.8%
3.2657
 
0.8%
3.3256
 
0.8%
3.5655
 
0.8%
3.3654
 
0.8%
3.3453
 
0.7%
3.251
 
0.7%
3.9651
 
0.7%
3.4751
 
0.7%
3.550
 
0.7%
Other values (469)6524
91.8%
ValueCountFrequency (%)
21
 
< 0.1%
2.062
 
< 0.1%
2.091
 
< 0.1%
2.111
 
< 0.1%
2.183
< 0.1%
2.1951
 
< 0.1%
2.21
 
< 0.1%
2.214
0.1%
2.225
0.1%
2.231
 
< 0.1%
ValueCountFrequency (%)
4.971
< 0.1%
4.951
< 0.1%
4.941
< 0.1%
4.931
< 0.1%
4.91
< 0.1%
4.871
< 0.1%
4.8651
< 0.1%
4.851
< 0.1%
4.832
< 0.1%
4.821
< 0.1%

REG_FEE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7038
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean376938.3307
Minimum71177
Maximum983922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:20.051470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum71177
5-th percentile197984.6
Q1272406
median349486
Q3451562
95-th percentile669167.4
Maximum983922
Range912745
Interquartile range (IQR)179156

Descriptive statistics

Standard deviation143070.662
Coefficient of variation (CV)0.3795598652
Kurtosis1.126499412
Mean376938.3307
Median Absolute Deviation (MAD)85998
Skewness1.037754561
Sum2679654593
Variance2.046921433 × 1010
MonotonicityNot monotonic
2022-07-05T09:51:20.123466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2352293
 
< 0.1%
3040662
 
< 0.1%
2910852
 
< 0.1%
4576062
 
< 0.1%
3511942
 
< 0.1%
2649142
 
< 0.1%
3009492
 
< 0.1%
3248032
 
< 0.1%
4196302
 
< 0.1%
3030812
 
< 0.1%
Other values (7028)7088
99.7%
ValueCountFrequency (%)
711771
< 0.1%
957981
< 0.1%
1039281
< 0.1%
1064661
< 0.1%
1113661
< 0.1%
1116901
< 0.1%
1129601
< 0.1%
1137591
< 0.1%
1140111
< 0.1%
1142101
< 0.1%
ValueCountFrequency (%)
9839221
< 0.1%
9811171
< 0.1%
9630291
< 0.1%
9524111
< 0.1%
9471241
< 0.1%
9428591
< 0.1%
9415671
< 0.1%
9408131
< 0.1%
9363141
< 0.1%
9312241
< 0.1%

COMMIS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7011
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141005.7265
Minimum5055
Maximum495405
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:20.199212image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum5055
5-th percentile35990.6
Q184219
median127628
Q3184506
95-th percentile292538
Maximum495405
Range490350
Interquartile range (IQR)100287

Descriptive statistics

Standard deviation78768.09372
Coefficient of variation (CV)0.558616275
Kurtosis1.073363345
Mean141005.7265
Median Absolute Deviation (MAD)49095
Skewness0.9516562165
Sum1002409710
Variance6204412588
MonotonicityNot monotonic
2022-07-05T09:51:20.283933image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1178253
 
< 0.1%
508042
 
< 0.1%
1551242
 
< 0.1%
1480172
 
< 0.1%
1206002
 
< 0.1%
1118572
 
< 0.1%
2236202
 
< 0.1%
727462
 
< 0.1%
1670802
 
< 0.1%
1324572
 
< 0.1%
Other values (7001)7088
99.7%
ValueCountFrequency (%)
50551
< 0.1%
51261
< 0.1%
53781
< 0.1%
56201
< 0.1%
59431
< 0.1%
60381
< 0.1%
61491
< 0.1%
61901
< 0.1%
62361
< 0.1%
63491
< 0.1%
ValueCountFrequency (%)
4954051
< 0.1%
4919611
< 0.1%
4859241
< 0.1%
4810011
< 0.1%
4792971
< 0.1%
4757951
< 0.1%
4712471
< 0.1%
4707841
< 0.1%
4699201
< 0.1%
4661561
< 0.1%

SALES_PRICE
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7057
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10894909.64
Minimum2156875
Maximum23667340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size55.7 KiB
2022-07-05T09:51:20.362168image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2156875
5-th percentile5630100
Q18272100
median10335050
Q312993900
95-th percentile18790428
Maximum23667340
Range21510465
Interquartile range (IQR)4721800

Descriptive statistics

Standard deviation3768603.457
Coefficient of variation (CV)0.345904976
Kurtosis0.5881293416
Mean10894909.64
Median Absolute Deviation (MAD)2317605
Skewness0.7733433359
Sum7.745191262 × 1010
Variance1.420237202 × 1013
MonotonicityNot monotonic
2022-07-05T09:51:20.439795image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53780002
 
< 0.1%
94290002
 
< 0.1%
49712502
 
< 0.1%
45466252
 
< 0.1%
119308802
 
< 0.1%
70747502
 
< 0.1%
67655002
 
< 0.1%
76385002
 
< 0.1%
57102502
 
< 0.1%
94628502
 
< 0.1%
Other values (7047)7089
99.7%
ValueCountFrequency (%)
21568751
< 0.1%
24763751
< 0.1%
26402501
< 0.1%
27972501
< 0.1%
29397501
< 0.1%
30003751
< 0.1%
30012501
< 0.1%
30135001
< 0.1%
30297501
< 0.1%
30813751
< 0.1%
ValueCountFrequency (%)
236673401
< 0.1%
234078601
< 0.1%
233145801
< 0.1%
233070001
< 0.1%
232475901
< 0.1%
230135001
< 0.1%
229185001
< 0.1%
229165001
< 0.1%
228528901
< 0.1%
228291301
< 0.1%

Interactions

2022-07-05T09:51:16.247607image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:05.973987image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:07.008106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:07.903300image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:08.906629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:09.752106image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2022-07-05T09:51:10.669081image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
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Correlations

2022-07-05T09:51:20.516543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-05T09:51:20.627173image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-05T09:51:20.732819image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-05T09:51:20.840469image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-05T09:51:20.931167image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-05T09:51:17.151073image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-05T09:51:17.410206image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-05T09:51:17.555025image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-05T09:51:17.622796image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PRT_IDAREAINT_SQFTDATE_SALEDIST_MAINROADN_BEDROOMN_BATHROOMN_ROOMSALE_CONDPARK_FACILDATE_BUILDBUILDTYPEUTILITY_AVAILSTREETMZZONEQS_ROOMSQS_BATHROOMQS_BEDROOMQS_OVERALLREG_FEECOMMISSALES_PRICE
0P03210Karapakkam100404-05-20111311.01.03AbNormalYes15-05-1967CommercialAllPubPavedA4.03.94.94.3303800001444007600000
1P09411Anna Nagar198619-12-2006262.01.05AbNormalNo22-12-1995CommercialAllPubGravelRH4.94.22.53.76576012230404921717770
2P01812Adyar90904-02-2012701.01.03AbNormalYes09-02-1992CommercialELOGravelRL4.13.82.23.0904210949211413159200
3P05346Velachery185513-03-2010143.02.05FamilyNo18-03-1988OthersNoSewrPavedI4.73.93.64.010356321770429630290
4P06210Karapakkam122605-10-2009841.01.03AbNormalYes13-10-1979OthersAllPubGravelC3.02.54.13.290237000740637406250
5P00219Chrompet122011-09-2014362.01.04PartialNo12-09-2009CommercialNoSeWaNo AccessRH4.52.63.13.32040902719831612394750
6P09105Chrompet116705-04-20071371.01.03PartialNo12-04-1979OtherAllPubNo AccessRL3.62.12.52.670263152339558488790
7P09679Velachery184713-03-20061763.02.05FamilyNo15-03-1996CommercialAllPubGravelRM2.44.52.13.26060480923520416800250
8P03377Chrompet77106-04-20111751.01.02AdjLandNo14-04-1977OthersNoSewrPavedRM2.93.74.03.550257578332368308970
9P09623Velachery163522-06-2006742.01.04AbNormalNo26-06-1991OthersELONo AccessI3.13.13.33.1603233461212558083650

Last rows

PRT_IDAREAINT_SQFTDATE_SALEDIST_MAINROADN_BEDROOMN_BATHROOMN_ROOMSALE_CONDPARK_FACILDATE_BUILDBUILDTYPEUTILITY_AVAILSTREETMZZONEQS_ROOMSQS_BATHROOMQS_BEDROOMQS_OVERALLREG_FEECOMMISSALES_PRICE
7099P03828Adyar89505-01-20111971.01.03AdjLandYes15-01-1971HouseNoSewrNo AccessI3.64.74.24.1225064173727371800
7100P05438T Nagar173324-02-20101911.01.04AbNormalYes02-03-1985CommercialNoSeWaNo AccessRL3.43.72.12.8970205831202619501600
7101P05042Karapakkam66611-05-2010511.01.02AdjLandYes20-05-1974OthersELOGravelI3.24.42.53.28273317745416211750
7102P05560Karapakkam70103-02-20101001.01.02AbNormalNo08-02-1990HouseNoSeWaGravelRH4.23.02.02.962821751410885643500
7103P05133Karapakkam146223-04-2010682.02.04FamilyNo29-04-1986OthersNoSeWaGravelRM2.73.33.63.243567161783589387250
7104P03834Karapakkam59803-01-2011511.01.02AdjLandNo15-01-1962OthersELONo AccessRM3.02.22.42.522087671070605353000
7105P10000Velachery189708-04-2004523.02.05FamilyYes11-04-1995OthersNoSeWaNo AccessRH3.64.53.33.9234619120555110818480
7106P09594Velachery161425-08-20061522.01.04Normal SaleNo01-09-1978HouseNoSeWaGravelI4.34.22.93.843173541670288351410
7107P06508Karapakkam78703-08-2009401.01.02PartialYes11-08-1977CommercialELOPavedRL4.63.84.14.164253501190988507000
7108P09794Velachery189613-07-20051563.02.05PartialYes24-07-1961OthersELOPavedI3.13.54.33.64349177798129976480